Data science, a dynamic and rapidly growing field with expanding job growth, has wide variations among data science programs and conceptualizations of data scientists’ skills. Consequently, employers face challenges recruiting the right data scientists, due to misalignment between university preparation and industry needs. To explore this further, we present an alternate data science view and use it to investigate knowledge gaps. Text mining of U.S. university syllabi is used to identify knowledge and skills taught by U.S. universities and colleges. These are compared to data science terms and concepts identified in job postings from previous research. We find graduates are better prepared for soft skills, but ill-prepared for technical and analytical skills. The findings are used to discuss how universities and industry can bridge the gaps.
AgrawalAGansJGoldfarbA (2020) How to win with machine learning. Harvard Business Review. Available at: https://hbsp.harvard.edu/product/R2005L-PDF-ENG.
2.
BartonDCourtD (2012) Making advanced analytics work for you. Harvard Business Review90(10): 78–83.
3.
BatističSvan der LakenP (2019) History, evolution and future of big data and analytics: a bibliometric analysis of its relationship to performance in organizations. British Journal of Management30(2): 229–251.
4.
BermanFRutenbarRHailpernB, et al. (2018) Realizing the potential of data science. Communications of the ACM61(4): 67–72.
5.
BichlerMHeinzlAvan der AalstWM (2017) Business analytics and data science: once again?Business & Information Systems Engineering59(2): 77–79.
BonessoSBruniEGerliF (2020) When hard skills are not enough: behavioral competencies of data scientists and data analysts. In: BonessoSBruniEGerliF (eds) Behavioral Competencies of Digital Professionals: Understanding the Role of Emotional Intelligence. Cham: Springer International Publishing, pp.63–87. Available at: https://doi.org/10.1007/978-3-030-33578-6_4 (accessed 19 September 2024).
8.
BörnerKScrivnerOGallantM, et al. (2018) Skill discrepancies between research, education, and jobs reveal the critical need to supply soft skills for the data economy. Proceedings of the National Academy of Sciences115(50): 12630–12637.
9.
BorofskyR (2002) The four subfields: anthropologists as mythmakers. American Anthropologist104(2): 463–480.
10.
BowersMRCammJDChakrabortyG (2018) The evolution of analytics and implications for industry and academic programs. Interfaces48(6): 487–499.
11.
BreauxTMoritzJ (2021) The 2021 software developer shortage is coming. Communications of the ACM64(7): 39–41.
12.
CaoL (2017a) Data science: a comprehensive overview. ACM Computing Surveys50(3): 1–42.
13.
CaoL (2017b) Data science: challenges and directions. Communications of the ACM60(8): 59–68.
14.
CegielskiCGJones-FarmerLA (2016) Knowledge, skills, and abilities for entry-level business analytics positions: a multi-method study. Decision Sciences Journal of Innovative Education14(1): 91–118.
15.
ColumbusL (2017) IBM predicts demand for data scientists will soar 28% by 2020. IBM White Paper. Epub ahead of print 2017.
DharV (2013) Data science and prediction. Communications of the ACM56(12): 64–73.
18.
DonohoD (2017) 50 Years of data science. Journal of Computational and Graphical Statistics26(4): 745–766.
19.
DoolittlePESiudzinskiRA (2010) Recommended syllabus components: what do higher education faculty include in their syllabi?Journal on Excellence in College Teaching21(3): 29–61.
20.
DuMLiuNHuX (2020) Techniques for interpretable machine learning. Communications of the ACM63(1): 68–77.
21.
DubeyRGunasekaranA (2015) Education and training for successful career in big data and business analytics. Industrial and Commercial Training47(4): 174–181.
22.
El-DeebA (2022) The human side of the tech industry: key drivers behind the tech talent dilemma. ACM SIGSOFT Software Engineering Notes47(1): 10–11.
KlimbergRMcCulloughBD (2013) Business analytics—today’s green. Contemporary Perspectives in Data Mining1(1): 47–60.
27.
LaValleSLesserEShockleyR, et al. (2010) Big data, analytics and the path from insights to value. MIT Sloan Management Review. Epub ahead of print 2010.
28.
LeaheyEMoodyJ (2014) Sociological innovation through subfield integration. Social Currents1(3): 228–256.
29.
LeeDMTrauthEMFarwellD (1995) Critical skills and knowledge requirements of IS professionals: a joint academic/industry investigation. MIS Quarterly19(3): 313–340.
30.
LeonLSealKPrzasnyskiZ, et al. (2018) Skills and competencies required for jobs in business analytics: a content analysis of job advertisements using text mining. In: Operations and Service Management: Concepts, Methodologies, Tools, and Applications. IGI Global, pp.880–904. Available at: https://www.igi-global.com/chapter/skills-and-competencies-required-for-jobs-in-business-analytics/192513 (accessed 16 December 2023).
31.
McAfeeABrynjolfssonEDavenportTH, et al. (2012) Big data: the management revolution. Harvard Business Review90(10): 60–68.
32.
MillerS (2014) Collaborative approaches needed to close the big data skills gap. Journal of Organization Design3(1): 26.
33.
NguyenAGardnerLSheridanD (2020) Data analytics in higher education: an integrated view. Journal of Information Systems Education31(1): 61.
PersaudA (2021) Key competencies for big data analytics professions: a multimethod study. Information Technology & People34(1): 178–203.
36.
RadovilskyZHegdeVAcharyaA, et al. (2018) Skills requirements of business data analytics and data science jobs: a comparative analysis. Journal of Supply Chain and Operations Management16(1): 82.
37.
RamzanMJKhanSURInayat-Ur-Rehman, et al. (2021) A conceptual model to support the transmuters in acquiring the desired knowledge of a data scientist. IEEE Access9: 115335–115347.
38.
RamzanMJKhanSURUr-RehmanI, et al. (2023) Facilitating transmuters’ acquisition of data scientist knowledge based on their educational backgrounds: state-of-the-practice and challenges. Library Hi Tech41(4): 1119–1144.
SaltzJArmourFShardaR (2018) Data science roles and the types of data science programs. Communications of the Association for Information Systems43(1): 615–624.
41.
SealKCLeonLAPrzasnyskiZH, et al. (2020) Delivering business analytics competencies and skills: a supply side assessment. INFORMS Journal on Applied Analytics50(4): 239–254.
42.
Singh DubeyRPaulJTewariV (2021) The soft skills gap: a bottleneck in the talent supply in emerging economies. The International Journal of Human Resource Management 33(13): 1–32.
43.
SodhiMSSonB-GTangCS (2008) ASP, the art and science of practice: what employers demand from applicants for MBA-level supply chain jobs and the coverage of supply chain topics in MBA courses. Interfaces38(6): 469–484.
44.
SodhiMSSonB (2010) Content analysis of OR job advertisements to infer required skills. Journal of the Operational Research Society61(9): 1315–1327.
45.
SongI-YZhuY (2016) Big data and data science: what should we teach?Expert Systems33(4): 364–373.
46.
StoddenV (2020) The data science life cycle: a disciplined approach to advancing data science as a science. Communications of the ACM63(7): 58–66.
47.
StoddenVGuoPMaZ (2013) Toward reproducible computational research: an empirical analysis of data and code policy adoption by journals. PLoS One8(6): e67111.
48.
Tenenboim-WeinblattKLeeC (2020) Speaking across communication subfields. Journal of Communication70(3): 303–309.
